lintheyoung / RT-ODLab

YOLO Tutorial

Real-time General Object Detection

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We are trying to build our real-time general target detection code base based on the core concepts of YOLO. We have reproduced most of the YOLO series. In addition, we have also written an introductory tutorial on YOLO. We hope that by learning YOLO, a very popular general target detection framework, beginners can master the basic knowledge necessary to study general target detection.

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Requirements

• We recommend you to use Anaconda to create a conda environment:

conda create -n rtcdet python=3.6

• Then, activate the environment:

conda activate rtcdet

• Requirements:

pip install -r requirements.txt 

My environment:

• PyTorch = 1.9.1
• Torchvision = 0.10.1

At least, please make sure your torch is version 1.x.

Experiments

VOC

• Download VOC.

cd <RT-ODLab>
cd dataset/scripts/
sh VOC2007.sh
sh VOC2012.sh

• Check VOC

cd <RT-ODLab>
python dataset/voc.py

• Train on VOC

For example:

python train.py --cuda -d voc --root path/to/VOCdevkit -m yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale

COCO

• Download COCO.

cd <RT-ODLab>
cd dataset/scripts/
sh COCO2017.sh

• Check COCO

cd <RT-ODLab>
python dataset/coco.py

• Train on COCO

For example:

python train.py --cuda -d coco --root path/to/COCO -m yolov1 -bs 16 --max_epoch 150 --wp_epoch 1 --eval_epoch 10 --fp16 --ema --multi_scale

Train

Single GPU

sh train_single_gpu.sh

You can change the configurations of train_single_gpu.sh, according to your own situation.

You also can add --vis_tgt to check the images and targets during the training stage. For example:

python train.py --cuda -d coco --root path/to/coco -m yolov1 --vis_tgt

Multi GPUs

sh train_multi_gpus.sh

You can change the configurations of train_multi_gpus.sh, according to your own situation.

In the event of a training interruption, you can pass --resume the latest training weight path (None by default) to resume training. For example:

python train.py \
        --cuda \
        -d coco \
        -m yolov1 \
        -bs 16 \
        --max_epoch 300 \
        --wp_epoch 3 \
        --eval_epoch 10 \
        --ema \
        --fp16 \
        --resume weights/coco/yolov1/yolov1_epoch_151_39.24.pth

Then, training will continue from 151 epoch.

Test

python test.py -d coco \
               --cuda \
               -m yolov1 \
               --img_size 640 \
               --weight path/to/weight \
               --root path/to/dataset/ \
               --show

For YOLOv7, since it uses the RepConv in PaFPN, you can add --fuse_repconv to fuse the RepConv block.

python test.py -d coco \
               --cuda \
               -m yolov7_large \
               --fuse_repconv \
               --img_size 640 \
               --weight path/to/weight \
               --root path/to/dataset/ \
               --show

Evaluation

python eval.py -d coco-val \
               --cuda \
               -m yolov1 \
               --img_size 640 \
               --weight path/to/weight \
               --root path/to/dataset/ \
               --show

Demo

I have provide some images in data/demo/images/, so you can run following command to run a demo:

python demo.py --mode image \
               --path_to_img data/demo/images/ \
               --cuda \
               --img_size 640 \
               -m yolov2 \
               --weight path/to/weight \
               --show

If you want run a demo of streaming video detection, you need to set --mode to video, and give the path to video --path_to_vid。

python demo.py --mode video \
               --path_to_vid data/demo/videos/your_video \
               --cuda \
               --img_size 640 \
               -m yolov2 \
               --weight path/to/weight \
               --show \
               --gif

If you want run video detection with your camera, you need to set --mode to camera。

python demo.py --mode camera \
               --cuda \
               --img_size 640 \
               -m yolov2 \
               --weight path/to/weight \
               --show \
               --gif

Detection visualization

• Detector: YOLOv2

Command：

python demo.py --mode video \
                --path_to_vid ./dataset/demo/videos/000006.mp4 \
               --cuda \
               --img_size 640 \
               -m yolov2 \
               --weight path/to/weight \
               --show \
               --gif

Results:

Tracking

Our project also supports multi-object tracking tasks. We use the YOLO of this project as the detector, following the "tracking-by-detection" framework, and use the simple and efficient ByteTrack as the tracker.

• images tracking

python track.py --mode image \
                --path_to_img path/to/images/ \
                --cuda \
                -size 640 \
                -dt yolov2 \
                -tk byte_tracker \
                --weight path/to/coco_pretrained/ \
                --show \
                --gif

• video tracking

python track.py --mode video \
                --path_to_img path/to/video/ \
                --cuda \
                -size 640 \
                -dt yolov2 \
                -tk byte_tracker \
                --weight path/to/coco_pretrained/ \
                --show \
                --gif

• camera tracking

python track.py --mode camera \
                --cuda \
                -size 640 \
                -dt yolov2 \
                -tk byte_tracker \
                --weight path/to/coco_pretrained/ \
                --show \
                --gif

Tracking visualization

• Detector: YOLOv2
• Tracker: ByteTracker
• Device: i5-12500H CPU

Command：

python track.py --mode video \
                --path_to_img ./dataset/demo/videos/000006.mp4 \
                -size 640 \
                -dt yolov2 \
                -tk byte_tracker \
                --weight path/to/coco_pretrained/ \
                --show \
                --gif

Results:

Train on custom dataset

Besides the popular datasets, we can also train the model on ourself dataset. To achieve this goal, you should follow these steps:

• Step-1: Prepare the images (JPG/JPEG/PNG ...) and use labelimg to make XML format annotation files.

OurDataset
|_ train
|  |_ images     
|     |_ 0.jpg
|     |_ 1.jpg
|     |_ ...
|  |_ annotations
|     |_ 0.xml
|     |_ 1.xml
|     |_ ...
|_ val
|  |_ images     
|     |_ 0.jpg
|     |_ 1.jpg
|     |_ ...
|  |_ annotations
|     |_ 0.xml
|     |_ 1.xml
|     |_ ...
|  ...

• Step-2: Convert ourdataset to COCO format.

cd <PyTorch_YOLO_Tutorial_HOME>
cd tools
# convert train split
python convert_ours_to_coco.py --root path/to/dataset/ --split train
# convert val split
python convert_ours_to_coco.py --root path/to/dataset/ --split val

Then, we can get a train.json file and a val.json file, as shown below.

OurDataset
|_ train
|  |_ images     
|     |_ 0.jpg
|     |_ 1.jpg
|     |_ ...
|  |_ annotations
|     |_ 0.xml
|     |_ 1.xml
|     |_ ...
|     |_ train.json
|_ val
|  |_ images     
|     |_ 0.jpg
|     |_ 1.jpg
|     |_ ...
|  |_ annotations
|     |_ 0.xml
|     |_ 1.xml
|     |_ ...
|     |_ val.json
|  ...

• Step-3 Define our class labels.

Please open dataset/ourdataset.py file and change our_class_labels = ('cat',) according to our definition of categories.

• Step-4 Check

cd <PyTorch_YOLO_Tutorial_HOME>
cd dataset
# convert train split
python ourdataset.py --root path/to/dataset/ --split train
# convert val split
python ourdataset.py --root path/to/dataset/ --split val

• Step-5 Train

For example:

cd <PyTorch_YOLO_Tutorial_HOME>
python train.py --root path/to/dataset/ -d ourdataset -m yolov1 -bs 16 --max_epoch 100 --wp_epoch 1 --eval_epoch 5 -p path/to/yolov1_coco.pth

• Step-6 Test

For example:

cd <PyTorch_YOLO_Tutorial_HOME>
python test.py --root path/to/dataset/ -d ourdataset -m yolov1 --weight path/to/checkpoint --show

• Step-7 Eval

For example:

cd <PyTorch_YOLO_Tutorial_HOME>
python eval.py --root path/to/dataset/ -d ourdataset -m yolov1 --weight path/to/checkpoint

Deployment

1. ONNX export and an ONNXRuntime